Knowledge of a patient's respiratory status is important in many medical applications. For example, the respiration rate and/or phase of respiration (i.e., inspiration, expiration, end-expiration, etc.) of a patient can be used to assess a patient's clinical status or for diagnostic purposes. In addition, knowledge of respiratory phase can be used to assist cardiac imaging or diagnostic electrocardiogram (ECG) acquisition. Since respiration causes movement of the heart within the thorax, respiratory phase information can be used to gate image or ECG acquisition to a "quiet" or stable phase.
Respiratory information is traditionally determined by processing a respiration waveform obtained from a respiration sensor and/or specialized electronic hardware. One such sensor, a pneumotach, measures the flow of air with a tube through which the subject breathes. Mechanical sensors or strain gauges have been used to provide signals related to respiration by measuring the expansion and contraction of the chest with each breath. An electronic method traditionally used in conjunction with ECG monitoring involves measuring the electrical impedance through the torso to detect conductivity changes that occur as a result of respiration (mainly due to air in the lungs). A drawback of the above methods is that they require specialized hardware to obtain the respiratory signals.
ECG signals are routinely acquired and/or monitored in numerous applications. A number of methods have been developed to obtain the respiration waveform from an ECG waveform. Typically, these methods derive the respiratory information from the cardiac axis, the changes in ECG amplitude, or both the amplitude and R-R interval information in the ECG waveform. Such methods have a number of shortcomings.
The derivation of respiratory signals from ECG waveforms has been reported. The nature of the body surface ECG depends on the electrical activity of the heart, the physical geometry between the heart and the recording electrodes, and the conductivity of the torso. Respiration causes variation in the ECG since respiration affects heart-electrode geometry (due to motion of the heart resting on the diaphragm and chest expansion), conductivity (primarily due to filling of the lungs with air), and blood volumes in the ventricles. Studies have shown, however, that it is primarily the change in heart position which affects the ECG, with lung conductivity and ventricular volume changes having a minor influence (J. Amoore, Y. Rudy, J. Liebman, "Respiration and the ECG: A Study Using Body Surface Potential Maps," Journal of Electrocardiology 21 (3) 263-271, 1988).
In their work to create "virtual ECG leads" (representing the ECG signals which would come from electrodes fixed in position relative to the heart), Pinciroli et al. studied the electrical axis of the heart and whether variations in the electrical axis could be used to derive respiratory signals (F. Pinciroli, R. Rossi, L, Vergani, "Detection of Electrical Axis Variation for the Extraction of Respiratory Information," Computers in Cardiology, 1985). They defined the electrical axis as "the direction of prevalent development of the heart's cardiac activity" and that it is "defined by the straight line that best interpolates the ECG loops obtained from a pair of ECG traces according to the least square criterion." Pinciroli et al. computed the angle between this line and a reference direction for each beat, plotted the time series, and compared it to respiration curves obtained from a belt impedance meter. Moody employed a similar technique, but used the area of each normal QRS complex in each of two leads measured over a fixed time window to compute the mean axis (G. Moody, R. Mark, A. Zoccola, S. Mantaro, "Derivation of Respiratory Signals From Multi-Lead ECGs," Computers in Cardiology, 1985).
Techniques similar to the above for deriving respiration information from ECG signals have a number of shortcomings. For example, there are numerous methods for computing the mean electrical axis and the resulting angles and they are not equivalent in constructing a respiratory curve. Selection among sets of angles is not straightforward. Also, multiple orthogonal ECG leads are required, or a lead must be placed so that its axis is significantly different from the mean electrical axis to obtain a relatively large respiratory signal. Also, the respiratory curve can be adversely affected by ectopic beats, arrhythmias, or even a slow heart rate (due to undersampling).
Varanini et al. studied the use of an adaptive filter to derive the respiratory signal from a single ECG lead (M. Varanini, M. Emdin, et al., "Adaptive Filtering of ECG Signal for Deriving Respiratory Activity," Computers in Cardiology, 1990). They used the R-R interval and the R-wave amplitude time series extracted from the ECG signal as the inputs to the filter. The R-R interval series contains variations due to respiratory sinus arrhythmia (RSA), which is a modulation of the heart rate by the autonomic nervous system in response to respiration induced effects (e.g., baroreceptor influences). LMS (Least Mean Square) and RLS (Recursive Least Square) adaptive filtering methods were applied to obtain the estimate of the respiratory signal. However, such techniques suffer from convergence and stability problems, and tradeoffs must be made between the two. They also will be sensitive to undersampling problems if the cardiac to respiratory rate falls below a ratio of 2:1.
Khaled et al. described a simple amplitude demodulation technique to derive the respiratory signal from a single ECG lead (Z. Khaled, G. Farges, "First Approach for Respiratory Monitoring by Amplitude Demodulation of the Electrocardiogram," Proc. Annual Intl. Conf. IEEE Engineering in Medicine & Biology Soc., 1992). The ECG was first high-pass filtered to remove baseline wander. A peak detector then controlled a sample-and-hold circuit to hold a voltage level at the beat peak. This resulted in a step-wise waveform, which was considered to be the respiration waveform. In order for the Khaled et al. technique to work, the ECG lead must contain sufficient respiration-induced amplitude modulation. However, sufficient respiration-induced amplitude modulation may not always be present. Such a technique also suffers from a sensitivity to undersampling due to the cardiac-respiration frequency ratio. What is needed is a simple, reliable technique for determining the respiratory effort of a patient using ordinary ECG signals.